CARMA Short Courses

Short Courses Overview

Each CARMA short course is typically a two or two and a half day session on a research method or data analysis topic. CARMA Short Courses place an emphasis on hands-on experience and on the application of the methodology aimed at skills development through equal amount of lecture and lab-time. Instructors are leading methodological scholars recognized within the organizational studies and management areas as experts on their topics. Several are current or past editors of leading organizational journals. Our list of short courses include introductory and advanced training on topics that might not be readily available at your institution. In addition, our short courses provide students and faculty with the opportunity to network with leading scholars and other students/faculty in their areas of interest.

More than 1,200 faculty and students from universities throughout the world have attended CARMA Short Courses since the summer of 2004. Past locations of these courses include Virginia and Michigan in the United States, as well as Brazil, Australia, India, The Netherlands, Israel, and China.

CARMA Short Course Evaluations

Past Participants had this to say about the CARMA Short Course program:

  • “Expert instructors, lively instruction, good balance between presentation and application.”
  • “The instructors did a great job explaining (1) conceptual issues (2) statistical content; (3) actually working with the program package.”
  • “Instructors were really awesome, the program as a whole was very organized and on schedule”
  • “The instructor was terrific! The venue was excellent.”
  • “I really thought the course was outstanding in all regards! Very helpful to my own research/publication efforts.”
  • “Excellent instructor! Extremely knowledgeable. We were able to actually practice/apply the theory to an article. Great experience!”
  • “Superb program – great contribution to academic community around the country.”
  • “Everything was wonderful – thank you so much for creating a format through which the knowledge could be so easily and cleanly passed on.”
  • “Absolutely awesome! Well worth the time and money!”
  • “This is an excellent program that I will recommend to my colleagues. I really appreciated the hospitality, the quality of lectures/materials, and the friendliness and accessibility of the instructors. Thank you very much – I learned a lot.”

Upcoming Short Courses

Short Courses in Adelaide, Australia, November 18-22, 2019 – Two Sessions, Two Courses

Hosted by University of South Australia

Session 1: November 18-20 | Session 2: November 20-22

We offer two sessions which allows course participants the opportunity to take two back-to-back courses.

Session 1

Monday November 18 (all day), Tuesday November 19 (all day), and Wednesday November 20 (AM half day)

Session 2

Wednesday November 20 (PM half day), Thursday November 21 (all day), and Friday November 22 (all day)

Registration, Pricing, Advanced Registration Deadline

To register for 2019 CARMA Short Courses in Adelaide, Australia, you must first log in to your CARMA account (If you do not already have an account, please sign-up as a website user). Once you have logged in, and you are in the User Area, select “Purchase Short Course” on the right side of the page.

Non-member prices per course: *All prices are in US Dollars (USD)
• Faculty/Professional: $900.00
• Students: $700.00
CARMA Member prices per course
• Faculty/Professional: $450.00
• Students: $350.00

If your organization is not yet a member but would like to become one, please contact us directly at carma@ttu.edu.

All participants are eligible for the following discount:
Register for both sessions, receive $75 off the total price.

Advanced Registration Deadline is Friday, November 1, 2019. After this date, a $75.00 fee will be added to all registrations.

Short Courses in Columbia, South Carolina, January 9-11, 2020 – One Session, Nine Course Options

Hosted by University of South Carolina

Short Course Sessions and Groupings

All courses in a session are taught concurrently, so a participant can take only one course per session.

CARMA Workshop: Basics of R

This four-hour Workshop provides information on the package R to prepare attendees for follow-up training in CARMA Short Courses that use R. By attending this workshop, participants will learn basic skills for using the R Studio interface to: load and activate R packages, import and manage data, and create and execute syntax. Having these basic skills will allow Short Course participants to more easily learn about use of R for data analysis and will enable Short Course instructors to better plan and deliver their content. This Workshop is only available to those who will be attending one of the CARMA Short Courses to be held at the University of South Carolina (January 9-11, 2020), it will be offered in person January 8 from 2-6 pm, and it will also be available on-line. There is no separate registration fee for this workshop.

During this Basics of R Workshop, attendees will learn:
1. Using R through the R Studio interface
2. Importing data into R
3. R data sets (a.k.a data frames and tibbles)
4. Data types
5. Subsetting columns of data and selecting cases
6. Recoding data and dealing with missing data
7. Merging data (columns and rows)
8. Output objects
9. User defined functions
10. Getting help

CARMA Short Courses

Option 1: “Introduction to R and Data Analysis” – Dr. Scott Tonidandel, University of North Carolina-Charlotte

Course Description

This course will provide a gentle introduction to the R computing platform and the R-Studio interface. We will cover the basics of R such as importing and exporting data, understanding R data structures, and R packages. You will also learn strategies for data manipulation within R (compute, recode, selecting cases, etc.) and best practices for data management. We will work through examples of how to conduct basic statistical analyses in R (descriptive, correlation, regression, T-test, ANOVA) and graph those results. Finally, we will explore user-defined functions in R and lay the groundwork for understanding how to perform more complex analyses presented in other CARMA short courses.

Option 2: “Advanced Data Analysis with R” – Dr. Justin DeSimone, The University of Alabama

Course Description

This short course will begin with an introduction to linear regression analysis with R, including models for single/multiple predictors and model comparison techniques.  Particular attention will be paid to using regression to test models involving mediation and moderation, followed by consideration of advanced topics including multivariate regression, use of polynomial regression, logistic regression, and the general linear model. Exploratory factor analysis and MANOVA will also be covered. For all topics, examples will be discussed and assignments completed using either data provided by the instructor or by the short course participants.

Option 3: “Introduction to Data Mining with R” – Dr. Jeff Stanton, Syracuse University

Course Description

Data mining refers to the discovery of novel patterns in data – particularly in large, semi-structured or unstructured data sets. Data mining techniques can support theory development by uncovering connections among phenomena that would be challenging to find with a typical survey or experimental method. In this CARMA short course, we will use R and R-Studio to get started with data mining.

We will begin by briefly reviewing the basics of R, add on packages, and data mining concepts. I recommend that you take CARMA’s basic R introductory R course if you have no prior familiarity with programming languages. We will discuss the conceptual steps involved in data mining, and then use R to put some of those concepts to work open data sets I will provide. Students are welcome to bring their own data sets for experimentation on their own, but this is not required. We will examine data reduction, feature extraction, feature elimination, several forms of clustering, association rules mining, and text mining (including topic modeling). Time permitting, we will explore various classifiers and compare their performance to one another.

Students who participate successfully in this short course can expect to learn enough about data mining to begin experimenting with these tools in research and/or teaching. The ideal participant will have an interest in improving their skill with R, knowledge of basic descriptive and inferential statistics, and curiosity about exploring alternative, empirically driven strategies for analysis of large data sets.

Required Software: R (download here), R Studio (download here)

Option 4: “Introduction to Statistical Learning with Big Data in R” – Dr. Fred Oswald, Rice University

Course Description

Traditional statistical models, such as linear regression and ANOVA, attempt to make useful predictions about people (e.g., employees’ standing on job performance) and groups (e.g., how teams differed in their mean performance). These models are relatively simple (i.e., any complex predictive relationships get overlooked), yet they might also capitalize on chance (i.e., not predict in data independent of those data used to develop the model).

To overcome these potential limitations, a large class of statistical learning models have been developed, some of which you may have heard of: e.g., random forests, LASSO regression, and support vector machines. These models determine whether complex relationships in the data can be reliably detected and then used to make predictions superior to those from traditional models.

This CARMA short course is a hands-on experience, where you will use R and RStudio to analyze and interpret those models. [If you are not familiar with the basics of how to navigate and use R, then you are strongly recommended to take CARMA’s introductory R course.] We will use openly available data sets, R code that has already been developed, and we will discuss, run, interpret a variety of statistical learning models together. Time permitting, we will explore methods for comparing the performance of these statistical learning models one another.

This course will equip students with the skills to perform their own predictive modeling using statistical learning models. They can then apply these skills in their research, practice, and teaching.

Required Software: R (download here), R Studio (download here)

Option 5: “Introduction to SEM with LAVAAN” – Dr. Robert Vandenberg, University of Georgia

Course Description

This introductory course requires no previous knowledge of structural equation modeling (SEM), but participants should possess a strong understanding of regression AND have understanding about the basic data handling functions using R. All illustrations and in-class exercises will make use of the R LAVAAN package, and participants will be expected to have LAVAAN installed on their laptop computers prior to beginning of the course. No course time will be spent going over basic R data handling and installing the LAVAAN package. The course will start with an overview of the principals underlying SEM. Subsequently, we move into measurement model evaluation including confirmatory factor analysis (CFA). Time will be spent on interpreting the parameter estimates and comparing competing measurement models for correlated constructs. We will then move onto path model evaluation where paths representing “causal” relations are placed between the latent variables. Again, time will be spent on interpreting the various parameter estimates and determining whether the path models add anything above their underlying measurement models. If time permits, longitudinal models will be introduced.

Required Software: R installed with LAVAAN package

Option 6: “Introduction to Multilevel Analysis with R” – Dr. Paul Bliese, University of South Carolina

Course Description

The CARMA Multilevel Analysis short course provides both (1) the theoretical foundation, and (2) the resources and skills necessary to conduct a wide range of multilevel analyses. The course covers within-group agreement, nested 2-level multilevel modeling and growth modeling. All practical exercises are conducted in R. Participants are encouraged to bring datasets to the course and apply the principles to their specific areas of research.

Option 7: “Web Scraping: Data Collection and Analysis” – Dr. Richard Landers, University of Minnesota

Course Description

In this course, you will learn how to create novel datasets from information found for free on the internet using only R and your own computer. After a brief introduction to web architecture and web design, we will explore the collection of unstructured data by scraping web pages directly through several small hands-on projects. Next, we will explore the collection of structured data by learning how to send queries directly to service providers like Google, Facebook and Twitter via their APIs. Finally, we will conduct a complete scraping project from start to finish including some novel analytic approaches (e.g., automatic identification of gender for social media contributors, language processing to extract themes, and interactive visualization with a simple web app).

Option 8: “Systematic Reviews and Meta-Analysis with R” – Dr. Ernest O’Boyle, Indiana University

Course Description

Meta-analyses have now become a staple of research in the organizational sciences. Their purpose is to summarize and clarify the extant literature through systematic and transparent means. Meta-analyses help answer long-standing questions, address existing debates, and highlight opportunities for future research. Despite their prominence, knowledge and expertise in meta-analysis is still restricted to a relatively small group of scholars. This short course is intended to expand that group by familiarizing individuals with the key concepts and procedures of meta-analysis with a practical focus. Specifically, the goal is to provide the necessary tools to conduct and publish a meta-analysis/systematic review using best practices. We will cover how to; (a) develop research questions that can be addressed with meta-analysis, (b) conduct a thorough search of the literature, (c) provide accurate and reliable coding, (d) correct for various statistical artifacts, and (e) analyze bivariate relationships (e.g., correlations, mean differences) as well as multivariate ones using meta-regression and meta-SEM. The course is introductory, so no formal training in meta-analysis is needed. Familiarity with some basic statistical concepts such as sampling error, correlation, and variation is sufficient.

Required Software: R

Option 9: “Open Science and R: Principles and Practices” – Dr. George Banks, University of North Carolina Charlotte

Course Description

The open science revolution continues to gain momentum across the social and natural sciences, and in particular, the organizational sciences. This movement is driven in part by a crisis in confidence of scientific research. However, open science offers so much more to scholars and stakeholders of scientific work.  Open science  can serve to accelerate science, facilitate large scale collaboration, and aid individual research teams in conducting more rigorous and relevant work. This short course is intended to introduce open science concepts across the life cycle of research. After taking this course you will be able to engage in open science practices during the full research process and successfully leverage such practices in future journal submissions to demonstrate exceptional methodological rigor. We will cover (a) questionable research practices and publication bias, (b) study preregistration, registered reports, results-blind reviews, preprints, and how to use badges, (c) open data, proper annotation of analytic R code, reproducibility of analyses and transparency checklists, (d) Do’s and Dont’s for replication studies, (e) how to navigate open science platforms, such as the open science framework, large scale project collaboration in management, and finally (f) authorship and contributorship agreements. The course is introductory. Familiarity with some basic statistical concepts, such as null hypothesis significance testing is sufficient.

Required software: R

Registration Details

To register for 2020 CARMA Short Courses at the University of South Carolina, you must first log in to your CARMA account (If you do not already have an account, please sign-up as a website user). Once you have logged in, and you are in the User Area, select “Purchase Short Course” on the right side of the page.

The early registration date is December 6, 2019.

Price Per Course

Early Registration  Non-Member  CARMA Member*
Faculty/Professional $900.00 $450.00
Student $700.00 $350.00
Late Registration  Non-Member  CARMA Member* 
Faculty/Professional $1000.00 $500.00
Student $800.00 $400.00

*Not sure if your Institution is a CARMA Member? Universities in the US and Canada may check here.

Accommodations/Overnight Lodging Suggestions

Hotel Address Phone
Courtyard Columbia Downtown at USC 630 Assembly St (approximately 5 minute walk to Business School) (803) 779-7800
Hilton Columbia Center Hotel 924 Senate St (approximately 7 minute walk to Business School) (803) 744-7800
Inn at USC Wyndham Garden Columbia 1619 Pendelton St (approximately 15 minute walk to Business School
but they offer a complimentary shuttle service)
(803) 779-7779

Short Courses in Detroit, Michigan, June 1-6, 2020 – Two Sessions, Twelve Course Options

Hosted by Wayne State University

Session 1: June 1-3, Six Course Options | Session 2: June 4-6, Six Course Options

Short Course Sessions and Groupings

We offer two sessions which allows course participants the opportunity to take two back-to-back courses that complement one another. All courses in a session are taught concurrently, so a participant can take only one course per session.

Complete Course Listing

Session 1                                                                                                                  Session 2

Mon. June 1 (all day), Tue. June 2 (all day), and Wed. June 3 (half day)             Thr. June 4 (all day), Fri. June 5 (all day), and Sat. June 6 (half day)

  1. “Introduction to R and Data Analysis” – Dr. Scott Tonidandel, University of North Carolina-Charlotte
  2. “Introduction to Data Mining with R” – Dr. Jeff Stanton, Syracuse University
  3. “Introduction to SEM with R” – Dr. Larry Williams, Texas Tech University
  4. “Advanced SEM I with LAVAAN” – Dr. Robert Vandenberg, University of Georgia
  5. “Introduction to Multilevel Analysis with R” – Dr. James LeBreton, Pennsylvania State University 
  6. “Systematic Reviews and Meta-Analysis with R” – Dr. Ernest O’Boyle, Indiana University
  7. “Advanced Topics in Survey Research” – Dr. Christiane Spitzmueller, University of Houston 
  8. “Web Scraping: Data Collection and Analysis with R” –Dr. Richard Landers, University of Minnesota
  1. “Advanced Data Analysis with R” – Dr. Ron Landis, Illinois Institute of Technology
  2. “Introduction to Statistical Learning with Big Data in R” – Dr. Fred Oswald, Rice University
  3. “Advanced SEM II with LAVAAN” – Dr. Robert Vandenberg, University of Georgia
  4. “Advanced Multilevel Analysis with R” – Dr. Paul Bliese,  University of South Carolina
  5. “Introduction to Bayesian Analysis with R” – Dr. Steve Culpepper, University of Illinois 
  6. “Questionnaire Design” – Dr. Lisa Schurer Lambert,  Oklahoma State University
  7. “Advanced Regression: Alternatives to Difference Scores, Polynomial Regression, and Response Surface Analysis” – Dr. Jeff Edwards, University of North Carolina
  8. “Open Science and R: Principles and Practices” – Dr. George Banks, University of North Carolina Charlotte

Qualitative Short Courses in Detroit, Michigan, June 2020 – Two Sessions, Eight Course Options

Hosted by Wayne State University

Session 1: June 2020, Four Course Options | Session 2: June 2020, Four Course Options

Short Course Sessions and Groupings

We offer two sessions which allows course participants the opportunity to take two back-to-back courses that complement one another. All courses in a session are taught concurrently, so a participant can take only one course per session.

Complete Course Listing

Session 1                                                                                                                  Session 2

  1. “Introduction to Qualitative Methods/Ethnography”
  2. “Text/Image Analysis and Computer Aided Qualitative Data Analysis Software (CAQDAS)”
  3. “Interviewing for Qualitative Research”
  4. “Mixed Methods”
  1. “Advanced Qualitative Analysis”
  2. “The Craft of Inductive Qualitative Research”
  3. “Grounded Theory Method and Analysis”
  4. “Qualitative Analysis for Organizational Change”
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